# with probability epsilon, explore
if(runif()<epsilon)
# shock your position by delta
position <- position + runif(min=-delta,max=delta)
else
# if a friend is doing better, play their slot machine
if(profits[me] < profits[best_friend])
position<- positions[best_friend]
# otherwise play the previous slot machine
play(position)# peek at everyone's profits and normalize them
masses<- (profits-min_profits)/(max_profits-min_profits)
# single out the top Z most profitable competitors
best<-order(masses)[1:Z]
# accelerate as if driven by "gravity" towards the best
acceleration<- acceleration+ sum(g * masses[me]*masses[best]/distance(me,best))
acceleration<- acceleration/masses[me]
# move (with some friction)
velocity<- velocity * runif() + acceleration
position<-position + velocity
play(position)\[ \text{Score} = \text{Blue Biomass}_{t=20} + \sum_{i=1}^{20} \text{Red Landings}_{t=i}\]
In a scenario where fishers are unable to respond to incentives the optimal quotas under TACs and ITQs are exactly the same